{"id":"W2788304950","doi":"10.48550/arxiv.1802.08626","title":"Empirical Risk Minimization under Fairness Constraints","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Empirical risk minimization; Mathematical optimization; Constraint (computer-aided design); Classifier (UML); Preprocessor; Consistency (knowledge bases); Machine learning; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02277327,0.001583189,0.002348584,0.001294314,0.001262779,0.004087751,0.003470603,0.002785583,0.003027223],"category_scores_gemma":[0.102162,0.0007340204,0.001083306,0.001516412,0.005074434,0.005691803,0.00585709,0.005408315,0.000830887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002807541,"about_ca_system_score_gemma":0.0035925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001454471,"about_ca_topic_score_gemma":0.0009733921,"domain_scores_codex":[0.9766632,0.01339373,0.0007984331,0.003504496,0.004721023,0.0009191193],"domain_scores_gemma":[0.9219426,0.05923849,0.004816178,0.008705822,0.004174095,0.001122888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002038039,0.0001914026,0.003198965,0.0002548636,0.0001824854,0.0001871236,0.0004738342,0.3929174,0.002116156,0.5104133,0.003609488,0.08625122],"study_design_scores_gemma":[0.00002479675,0.00005056768,0.0003375789,0.00003270541,0.00001571571,0.00006448384,0.00002969269,0.5559253,0.001297535,0.4406936,0.001509805,0.00001822988],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009097723,0.0002357776,0.9866802,0.0009341717,0.00004467541,0.00006046837,0.00004639699,0.00010785,0.002792811],"genre_scores_gemma":[0.6076159,0.000588254,0.3808124,0.001058319,0.0006477503,0.0006211011,0.0002612088,0.0003385094,0.008056583],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02277327,"threshold_uncertainty_score":0.120438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.194005217089235,"score_gpt":0.2999946769451043,"score_spread":0.1059894598558693,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}